What are some reasons to animate a plot?

Bad Reasons

• It’s neat
  
• I want to show off
  
• One of my dimensions is time

Good Reasons

• I have four or more meaningful data dimensions
  
• I want to display many levels of a single dimension

Also: If you need to see all the data points at the same time DON’T use animation

• data changing over a long period of time
• groups of datapoints varying systematically

Ask Yourself

• What information do you want the audience to take away?

library(ggplot2) # make plots
library(gganimate) # animate the plots
library(gifski) # render gifs
library(transformr) # insert smooth transformations

Frame

• Animations are a series of single images (frames) strung together

State

• The current grouping variable being shown. It may be one frame, or a single state representation can stay visible for multiple frames

Transition

• How the visual rendering moves from state to state

Enter/Exit

• How datapoints with no predecessor or follower are handled

Using data on displaced persons living in South Africa, show patterns and trends in refugee movement over twenty-plus years.

Task: show complex data across many levels of a facet

Problems: Facet grid and facet wrap rapidly get out of their depth

Follow along with the notebook: https://github.com/skirmer/animating_dataviz/blob/master/ioslides_deck.Rmd

Data Source: UNHCR via data.world: UNHCR’s populations of concern residing in South Africa

Grab dataset of displaced persons living in South Africa

Select the top 10 countries of origin for each year, apply some filters.

library(magrittr)
library(dplyr)

plotDT <- dtset %>%
  filter(X_country_origin != "Various/Unknown") %>%
  filter(X_population_type == "Refugees (incl. refugee-like situations)") %>%
  filter(X_date_year > 1995) %>%
  group_by(X_date_year) %>%
  mutate(rank = rank(-X_affected, ties.method = "first") * 1) %>%
  ungroup() %>%
  filter(rank <= 10) %>%
  data.frame()

Group by country, bar height is affected persons

baseplot1 <- ggplot(plotDT, 
aes(X_date_year, 
    group = X_country_origin, 
    fill = as.factor(X_country_origin), 
    color = as.factor(X_country_origin)))+
theme_bw()+
theme(legend.position = "bottom")+
geom_bar(aes(y = X_affected), stat = "identity", position = "dodge")

Ew. This is not effective.

Let’s make this!

baseplot2 <- baseplot1 +
    coord_flip(clip = "off", expand = FALSE, ylim = c(0, 50000))

Can’t use the same stub now.

baseplot3 <- ggplot(plotDT, 
aes(x=rank, 
    group = X_country_origin, 
    fill = as.factor(X_country_origin), 
    color = as.factor(X_country_origin)))+
theme_bw()+
theme(legend.position = "bottom")+
geom_text(aes(y = 0, label = paste(X_country_origin, " ")), vjust = 0.2, hjust = 1) +
coord_flip(clip = "off", expand = FALSE, ylim = c(0, 50000)) +
geom_bar(aes(y = X_affected), stat = "identity", position = "identity")

Left side: grouped bar, sorted by year
Right side: not grouped bar, sorted by rank

If your stub has a theme() segment, applying a new one will overrule it.

baseplot3 <- baseplot3 + 
theme(legend.position = "none",
    axis.ticks.y = element_blank(),  # These relate to the axes post-flip
    axis.text.y  = element_blank(),  # These relate to the axes post-flip
    axis.title.y = element_blank(),
    plot.margin = margin(1,1,1,5, "cm"))

baseplot3 <- baseplot3 +
scale_y_continuous(labels = scales::comma) +
scale_x_reverse()

Now we have the different “frames” all layered on top of each other.

We need to present the data on an annual basis in a way that tells us:

1. What locations refugees come from
   • These may be places with high instability or political turmoil at a given time
2. How the top locations within a year compare to the other top 10 locations

No.

Definitely not.

Literally add one more line of code to your ggplot object.

animp <- baseplot3 +
transition_states(X_date_year)

It’s nice, but we can do better

1. Viewer can’t tell what the frames represent
   
2. Y axis needs labeling
   
3. Bars need clear labels
   
4. It’s moving abruptly between frames

Solving Problem 1 and 2: Added a descriptive title/label that indicate the year of the frame

animp <- baseplot3 +
  geom_text(aes(y = X_affected, 
    label = as.character(X_affected)), 
    color = "black", vjust = 0.2, hjust = -.5)+
  labs(title = "Refugees Residing in South Africa by Origin, {closest_state}"
    , y="Affected Persons")+
  transition_states(X_date_year)

Solving Problem 3: how do we want the animation elements to move?

Option: shrink and grow on exit and enter

animp <- baseplot3 +
  geom_text(aes(y = X_affected, 
    label = as.character(X_affected)), 
    color = "black", vjust = 0.2, hjust = -.5)+
  labs(title = "Refugees Residing in South Africa by Origin, {closest_state}"
    , y="Affected Persons")+
  transition_states(X_date_year)+
  enter_grow() +
  exit_shrink()

It’s interesting, but probably not serving the project objectives

Solving Problem 3: how do we want the animation elements to move?

Option: Ease between positions (moving on page, not exiting or entering)

animp <- baseplot3 +
  geom_text(aes(y = X_affected, 
    label = as.character(X_affected)), 
    color = "black", vjust = 0.2, hjust = -.5)+
  labs(title = "Refugees Residing in South Africa by Origin, {closest_state}"
  , y="Affected Persons")+
  transition_states(X_date_year)+
  ease_aes('quartic-in-out')

Hopefully, the movement feels less abrasive to the eye now

Solving Problem 3: how do we want the animation elements to move?

Option: For fun, let’s try “back” to see a springier approach

animp <- baseplot3 +
  geom_text(aes(y = X_affected, 
    label = as.character(X_affected)), 
    color = "black", vjust = 0.2, hjust = -.5)+
  labs(title = "Refugees Residing in South Africa by Origin, {closest_state}"
  , y="Affected Persons")+
  transition_states(X_date_year)+
  ease_aes('back-in-out')

Feels a little cartoony- interesting, but again perhaps not what we need

Solving Problem 3: how do we want the animation elements to move?

In addition to entry, exit, and transition easing:

• Set pace for the states/transitions
  
• Ensure no smoothing between the end and restarting

animp <- baseplot3 +
  geom_text(aes(y = X_affected, 
    label = as.character(X_affected)), 
    color = "black", vjust = 0.2, hjust = -.5)+
  labs(title = "Refugees Residing in South Africa by Origin, {closest_state}"
    , y="Affected Persons")+
  transition_states(X_date_year,transition_length = 5, 
      state_length = c(rep(.25, 21), 20), wrap = FALSE)+
  ease_aes('linear')+
  enter_fade() +
  exit_fade()

Slower pace feels smoother, and doesn’t insinuate that the last frame and the first flow into each other

  transition_states(
    X_date_year,
    transition_length = 5, 
    state_length = c(rep(.25, 21), 20),
    wrap = FALSE)+

Assign the frame unit- here we use the year.

Transition length is the period of time we use for change from frame to frame
- slow it down if you want a smooth looking animation

State length is the period of time where the frame stays static.
- here I am making the very last frame stay static longer

Wrap determines whether to apply transition smoothing between the end and restarting

Experiment with these settings to get the look you want!

animate(animp, fps = 10, duration = 20, width = 800, height = 450) 

anim_save(filename = "final_race_plot2.gif")

Figure out the in/out transitions that make sense and are not misleading/distracting

Give the eye enough time to understand each state

Make your plot serve the audience, don’t be fancy if it’s not helpful

https://gganimate.com/

https://ggplot2.tidyverse.org/